Literature DB >> 31759796

Artificial Intelligence, Radiology, and Tuberculosis: A Review.

Sagar Kulkarni1, Saurabh Jha2.   

Abstract

Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridge this gap. In this article, we review the applications of artificial intelligence in the diagnosis of tuberculosis using chest radiography, covering simple computer-aided diagnosis systems to more advanced deep learning algorithms. In so doing, we will demonstrate an area where artificial intelligence could make a substantial contribution to global health through improved diagnosis in the future.
Copyright © 2019 The Association of University Radiologists. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Computer-aided diagnosis; Deep learning; Global health; Tuberculosis

Mesh:

Year:  2019        PMID: 31759796     DOI: 10.1016/j.acra.2019.10.003

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  6 in total

Review 1.  A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.

Authors:  Gopi Battineni; Mohmmad Amran Hossain; Nalini Chintalapudi; Francesco Amenta
Journal:  Diagnostics (Basel)       Date:  2022-05-09

2.  Diagnostic accuracy of point-of-care ultrasound for pulmonary tuberculosis: A systematic review.

Authors:  Jacob Bigio; Mikashmi Kohli; Joel Shyam Klinton; Emily MacLean; Genevieve Gore; Peter M Small; Morten Ruhwald; Stefan Fabian Weber; Saurabh Jha; Madhukar Pai
Journal:  PLoS One       Date:  2021-05-07       Impact factor: 3.240

Review 3.  Applications and challenges of artificial intelligence in diagnostic and interventional radiology.

Authors:  Joseph Waller; Aisling O'Connor; Eleeza Rafaat; Ahmad Amireh; John Dempsey; Clarissa Martin; Muhammad Umair
Journal:  Pol J Radiol       Date:  2022-02-25

4.  Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis.

Authors:  Andrew J Codlin; Thang Phuoc Dao; Luan Nguyen Quang Vo; Rachel J Forse; Vinh Van Truong; Ha Minh Dang; Lan Huu Nguyen; Hoa Binh Nguyen; Nhung Viet Nguyen; Kristi Sidney-Annerstedt; Bertie Squire; Knut Lönnroth; Maxine Caws
Journal:  Sci Rep       Date:  2021-12-13       Impact factor: 4.379

5.  Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence.

Authors:  Saad I Nafisah; Ghulam Muhammad
Journal:  Neural Comput Appl       Date:  2022-04-19       Impact factor: 5.102

6.  Characterization of antibody response against 16kD and 38kD of M. tuberculosis in the assisted diagnosis of active pulmonary tuberculosis.

Authors:  Xiaohui Hao; Jie Bai; Yingying Ding; Jinhong Wang; Yidian Liu; Lan Yao; Wei Pan
Journal:  Ann Transl Med       Date:  2020-08
  6 in total

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